code: def get_pred(
      self,
      logits: torch.Tensor,
      targets: torch.Tensor,
      contexts: Dict[str, List[Any]],
  ) -> Tuple[
      Tuple[torch.Tensor, torch.Tensor],
      Tuple[torch.Tensor, torch.Tensor],
  ]:
      start_pos_logits, end_pos_logits, has_answer_logits, _, _ = logits
      start_pos_preds, end_pos_preds = self.get_position_preds(
          start_pos_logits, end_pos_logits, self.max_answer_len
      )
      has_answer_preds = has_answer_logits.float().argmax(-1)
      has_answer_scores = torch.zeros(has_answer_logits.size())
      if not self.ignore_impossible:
          has_answer_scores = F.softmax(has_answer_logits, 1)
  
      # Compute the logit of the corresponding to start and end positions.
      start_pos_scores = (
          F.softmax(start_pos_logits, 1)
          .gather(1, start_pos_preds.view(-1, 1))
          .squeeze(-1)
      )
      end_pos_scores = (
          F.softmax(end_pos_logits, 1)
          .gather(1, end_pos_preds.view(-1, 1))
          .squeeze(-1)
      )
  
      return (
          (start_pos_preds, end_pos_preds, has_answer_preds),
          (start_pos_scores, end_pos_scores, has_answer_scores),
      )
  
file_path: pytext/pytext/models/output_layers/squad_output_layer.py
imports: from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from pytext.config.component import create_loss
  from pytext.fields import FieldMeta
  from pytext.loss import BinaryFocalLoss, CrossEntropyLoss, FocalLoss, KLDivergenceCELoss, Loss
  from pytext.models.output_layers import OutputLayerBase
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 4 for calling the SquadOutputLayer obj's underlying member method code with example usage: obj.get_pred(logits, targets, contexts) and returns: start_pos_preds,  end_pos_preds,  has_answer_preds,  start_pos_scores,  end_pos_scores,  has_answer_scores